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190 results for “computational imaging”
Bridging the analog divide: A comparison of printed X-ray films and digital images when using computer-aided detection software for tuberculosis screening
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4D light sheet imaging, computational reconstruction, and cell tracking in mouse embryos -- example data (raw .czi and fused .klb light sheet images of mouse E7.5)
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Four-dimensional computational ultrasound imaging of brain hemodynamics
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Targeted indication of imaging for detection of vesicoureteric reflux after pediatric febrile urinary tract infections based on a multiparametric computational tool
<p>This is the original data used for our study on differentiated indication of a voiding cystourethrography after pediatric febrile urinary tract infections. </p>
Data from: Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning
<p><b>Objectives: </b>To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation.</p> <p><b>Background: </b>Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images.</p> <p><b>Methods: </b>Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split.</p> <p><b>Results: </b>The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups.</p> <p><b>Conclusions: </b>An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level.</p>
FIGURES 11–12 in First fossil Micropholcommatidae (Araneae), imaged in Eocene Paris amber using X-Ray Computed Tomography
FIGURES 11–12. Photographs of Cenotextricella simoni sp. nov. (male holotype, MNHN PA 327) using traditional light microscopy. (1) dorsal view; (2) ventral view. See Fig. 5 for scale.
FIGURES 1–10 in First fossil Micropholcommatidae (Araneae), imaged in Eocene Paris amber using X-Ray Computed Tomography
FIGURES 1–10. VHR-CT scans of Cenotextricella simoni sp. nov. (male holotype, MNHN PA 327). (1) dorsal view; (2) ventral view; (3) anterior view; (4) posterior view; (5) lateral view; (6) lateral sectioned view; (7) right pedipalp dorsal view; (8) right pedipalp posterior view; (9) left pedipalp retrolateral view; (10) right pedipalp prolateral view. Abbreviations: ALE, anterior lateral eye; AME, anterior median eye; c, conductor; cy, cymbium; ds, dorsal abdominal scutum; e, embolus; fe, femur; mt/t, metatarsus/tarsus joint; pa, patella; PLE, posterior lateral eye; PME, posterior median eye; st, sternum; th, tibial hook; ti, tibia; vs, ventral abdominal scutum.
FIGURES 8–21 in A new species of anapid spider (Araneae: Araneoidea, Anapidae) in Eocene Baltic amber, imaged using phase contrast X-ray computed micro-tomography
FIGURES 8–21. CT reconstructions of Balticoroma wheateri new species (male holotype, GPIH). (8) frontal view showing chelicerae and labral spur; (9) view of right pedipalp showing embolus; (10–14) various views of right metatarsus 1, showing y-shaped clasping structure; (15) anterior view of specimen showing the section taken through the chelicerae to produce the raw data slice in Figure 16; (16) raw data slice demonstrating that the chelicerae and clypeal extentions are clearly separated; (20–21) various views of the right pedipalp. C, chelicera; ce, clypeal extension; co, dorsal cymbial outgrowth; cy, cymbium; e, embolus; eb, embolic base; ec, embolic coil;?fc, functional conductor sensu Wunderlich (2004); ls, labral spur; t, tegulum.
FIGURE 1 in A new species of anapid spider (Araneae: Araneoidea, Anapidae) in Eocene Baltic amber, imaged using phase contrast X-ray computed micro-tomography
FIGURE 1. Microphotograph of Balticoroma wheateri new species (male holotype, GPIH). Body length = 1.8 mm.
FIGURES 2–7 in A new species of anapid spider (Araneae: Araneoidea, Anapidae) in Eocene Baltic amber, imaged using phase contrast X-ray computed micro-tomography
FIGURES 2–7. CT reconstructions of Balticoroma wheateri new species (male holotype, GPIH). (2) right lateral view; (3) left lateral view; (4) dorsal view; (5) ventral view; (6) anterior view; (7) posterior view. Body length = 1.8 mm. Mt1, metatarsus 1; ta1, tarsus 1; ti1, tibia 1.
Frequency-dependent cortical plasticity: evidence from psychophysics, functional imaging and computational modelling.
<p>fMRI data relating to the paper entitled 'Frequency-dependent cortical plasticity: evidence from psychophysics, functional imaging and computational modelling'. </p>
Effects of individualized Electrical Impedance Tomography and image reconstruction settings upon the assessment of regional ventilation distribution: Comparison to 4-dimensional Computed Tomography in a porcine model
<p>Reconstruction Models used for identification of optimal settings for comparison to CT images. Forward models are available in the supplement of the article but were removed form the inverse models due to redundance storage within each model.</p> <p>Prior reconstruction in EIDORS, forward models have to be added again to<em> imdl.fwd_model</em> and <em>imdl.jacobian_background.fwd_model</em>.</p>
Data and models for "An image-computable model of speeded decision-making"
<p>Lost in Migration gameplay data and trained models for:</p> <div>Jaffe, P. I., Gustavo, X. S. R., Schafer, R. J., Bissett, P. G., Poldrack, R. A. An image-computable model of speeded decision-making. <em>eLife</em> <strong>13</strong>, RP98351 (2024).</div> <div> </div> <p>This dataset can be used to reproduce all of the results of the manuscript, following the instructions in the code repository for the paper: <a href="https://github.com/pauljaffe/vam">https://github.com/pauljaffe/vam</a>.</p> <p>The dataset includes the following components:</p> <p><strong>gameplay_data.zip:</strong> Trial-level gameplay metadata for Lost in Migration. Lost in Migration is a variant of the flanker task offered as a part of the Lumosity cognitive training platform (Lumos Labs, Inc.). The .zip file includes a separate .csv file for each of the 75 Lumosity users (participants) that we trained models on. Each .csv file has one row per trial with the following fields/columns: "anon_id", numerical identifier for the Lumosity user; "nth_play", the nth gameplay of Lost in Migration for this user; "trial", the nth trial for the current gameplay; "xpos", the signed horizontal distance from the center of the target bird to the left edge of the game window (pixels, non-negative); "ypos", the signed vertical distance from the center of the target bird to the bottom edge of the game window (pixels, non-negative); "flanker_direction", (L/R/U/D); "response_direction", (L/R/U/D); "target_direction", (L/R/U/D); "response_time", (ms); "stimulus_layout", numerical code for the layout of the bird flock for the current trial (0: horizontal line, 1: vertical line, 2: cross, 3: <, 4: >, 5: v, 6: ^)<strong>.</strong></p> <p><strong>vam_models.zip:</strong> Parameters for the 75 visual accumulator models (VAMs) analyzed in the manuscript.</p> <p><strong>task_opt_models.zip:</strong> Parameters for the 75 task-optimized models analyzed in the manuscript.</p> <p><strong>metadata.csv:</strong> Metadata for each Lumosity user that a VAM/task-optimized model was trained on. The .csv file has one row per user with the following fields/columns: "user_id", numerical identifier for the Lumosity user (same as "anon_id" in gameplay_data.zip); "gender", self-reported gender ('m', 'f', or null, indicating no response was given); "binned_age", age bucketed into decade-long bins (20-29, 30-39... 80-89).</p> <p><strong>derivatives.zip:</strong> The RTs/choices generated by the trained models, organized into separate folders by model type (vam/task_opt/binned_rt) and user ID. Also includes a "summary_stats" folder with analysis products of the model activations and outputs.</p> <p><strong>graphics.zip:</strong> Image files used to create the visual stimuli from the gameplay metadata.</p> <p><strong>example_model_inputs.zip: </strong>The processed visual stimuli and gameplay data used as inputs to train one model (user ID 182). Note we provide instructions to recreate the stimuli and other model inputs for all models in the code repository.</p>
Table A6 images from Computer-aided drug design (CADD) to de-orphanise marine molecules: Finding potential therapeutic agents for neurodegenerative and cardiovascular diseases
<p>High Reslution Images from Table A6</p>
In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH. in Deep learning brings speed, accuracy to the life sciences.
In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH.
dataset for root canal configuration of mandibular first and second premolars using in vivo cone-beam computed tomography imaging
<p>dataset for root canal configuration of mandibular first and second premolars using in vivo cone-beam computed tomography imaging</p>
FIGURE 5. Micro-computed tomography 3D images. A–D in A new species of sponge crab of the genus Epigodromia McLay 1993 (Crustacea: Brachyura: Dromiidae) from the southeastern Arabian Sea, with notes on the Zoogeography
FIGURE 5. Micro-computed tomography 3D images. A–D, dorsal view; B–C, frontal view with chelipeds outer view. A, B, Epigodromia mclayi sp. nov., holotype, male (cw 11.43 mm, cl 10.33 mm), (IO/SS/BRC00370), southeastern Arabian Sea, Tamil Nadu, India; C–D, Epigodromia gilesii Alcock, 1900, male (cw 5.8 mm, cl 6.05 mm), (IO/SS/BRC00372), Malabar coast, southeastern Arabian Sea, India.
In Vivo Rodent Cervicothoracic Vasculature Imaging Using Photoacoustic Computed Tomography
<p>These video clips are the supplementary video for the manuscript "<em>In Vivo</em> Rodent Cervicothoracic Vasculature Imaging Using Photoacoustic Computed Tomography" submitted to <em>Photonics</em>.</p>
High-resolution X-ray computed tomography images of Bentheim sandstone under elevated stress
<p>A dry sample of Bentheim (or Bentheimer) sandstone was characterized using 3D X-Ray microscopy (Versa XRM-500, XRadia-Zeiss) at three different confining pressures of 1 MPa, 20 MPa, and 30 MPa and two voxel sizes of (1.5854 µm)<sup>3</sup> and (3.3452 µm)<sup>3</sup>. The 5-mm-diameter, 20-mm-long dry sample was placed inside a custom-made pressure sell (Lebedev et al, 2017). The sample was subjected to confining pressure of 20 MPa and 3200 radiographs were acquired, then confining pressure was reduced to 1MPa and the sample was imaged again, finally, the sample was pressurized up to 30MPa and the final image set was taken. Image reconstruction was done using internal software (XRadia-Zeiss).</p>
Canine mammary tumors histopathological image classification by computer-aided pathology_ supplementary files
<p>Supplementary files</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.